{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import pdb\n",
    "\n",
    "import time\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import copy\n",
    "import math\n",
    "\n",
    "from cubic_spline_planner import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class quintic_polynomial:\n",
    "\n",
    "    def __init__(self, xs, vxs, axs, xe, vxe, axe, T):\n",
    "\n",
    "        # calc coefficient of quintic polynomial\n",
    "        self.xs = xs\n",
    "        self.vxs = vxs\n",
    "        self.axs = axs\n",
    "        self.xe = xe\n",
    "        self.vxe = vxe\n",
    "        self.axe = axe\n",
    "\n",
    "        self.a0 = xs\n",
    "        self.a1 = vxs\n",
    "        self.a2 = axs / 2.0\n",
    "\n",
    "        A = np.array([[T**3, T**4, T**5],\n",
    "                      [3 * T ** 2, 4 * T ** 3, 5 * T ** 4],\n",
    "                      [6 * T, 12 * T ** 2, 20 * T ** 3]])\n",
    "        b = np.array([xe - self.a0 - self.a1 * T - self.a2 * T**2,\n",
    "                      vxe - self.a1 - 2 * self.a2 * T,\n",
    "                      axe - 2 * self.a2])\n",
    "        x = np.linalg.solve(A, b)\n",
    "\n",
    "        self.a3 = x[0]\n",
    "        self.a4 = x[1]\n",
    "        self.a5 = x[2]\n",
    "\n",
    "    def calc_point(self, t):\n",
    "        xt = self.a0 + self.a1 * t + self.a2 * t**2 + \\\n",
    "            self.a3 * t**3 + self.a4 * t**4 + self.a5 * t**5\n",
    "\n",
    "        return xt\n",
    "\n",
    "    def calc_first_derivative(self, t):\n",
    "        xt = self.a1 + 2 * self.a2 * t + \\\n",
    "            3 * self.a3 * t**2 + 4 * self.a4 * t**3 + 5 * self.a5 * t**4\n",
    "\n",
    "        return xt\n",
    "\n",
    "    def calc_second_derivative(self, t):\n",
    "        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 + 20 * self.a5 * t**3\n",
    "\n",
    "        return xt\n",
    "\n",
    "    def calc_third_derivative(self, t):\n",
    "        xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t**2\n",
    "\n",
    "        return xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class quartic_polynomial:\n",
    "\n",
    "    def __init__(self, xs, vxs, axs, vxe, axe, T):\n",
    "\n",
    "        # calc coefficient of quintic polynomial\n",
    "        self.xs = xs\n",
    "        self.vxs = vxs\n",
    "        self.axs = axs\n",
    "        self.vxe = vxe\n",
    "        self.axe = axe\n",
    "\n",
    "        self.a0 = xs\n",
    "        self.a1 = vxs\n",
    "        self.a2 = axs / 2.0\n",
    "\n",
    "        A = np.array([[3 * T ** 2, 4 * T ** 3],\n",
    "                      [6 * T, 12 * T ** 2]])\n",
    "        b = np.array([vxe - self.a1 - 2 * self.a2 * T,\n",
    "                      axe - 2 * self.a2])\n",
    "        x = np.linalg.solve(A, b)\n",
    "\n",
    "        self.a3 = x[0]\n",
    "        self.a4 = x[1]\n",
    "\n",
    "    def calc_point(self, t):\n",
    "        xt = self.a0 + self.a1 * t + self.a2 * t**2 + \\\n",
    "            self.a3 * t**3 + self.a4 * t**4\n",
    "\n",
    "        return xt\n",
    "\n",
    "    def calc_first_derivative(self, t):\n",
    "        xt = self.a1 + 2 * self.a2 * t + \\\n",
    "            3 * self.a3 * t**2 + 4 * self.a4 * t**3\n",
    "\n",
    "        return xt\n",
    "\n",
    "    def calc_second_derivative(self, t):\n",
    "        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2\n",
    "\n",
    "        return xt\n",
    "\n",
    "    def calc_third_derivative(self, t):\n",
    "        xt = 6 * self.a3 + 24 * self.a4 * t\n",
    "\n",
    "        return xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Frenet_path:\n",
    "\n",
    "    def __init__(self):\n",
    "        self.t = []\n",
    "        self.d = []\n",
    "        self.d_d = []\n",
    "        self.d_dd = []\n",
    "        self.d_ddd = []\n",
    "        self.s = []\n",
    "        self.s_d = []\n",
    "        self.s_dd = []\n",
    "        self.s_ddd = []\n",
    "        self.cd = 0.0\n",
    "        self.cv = 0.0\n",
    "        self.cf = 0.0\n",
    "\n",
    "        self.x = []\n",
    "        self.y = []\n",
    "        self.yaw = []\n",
    "        self.ds = []\n",
    "        self.c = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Parameter\n",
    "MAX_SPEED = 50.0 / 3.6  # maximum speed [m/s]\n",
    "MAX_ACCEL = 2.0  # maximum acceleration [m/ss]\n",
    "MAX_CURVATURE = 1.0  # maximum curvature [1/m]\n",
    "MAX_ROAD_WIDTH = 7.0  # maximum road width [m]\n",
    "D_ROAD_W = 1.0  # road width sampling length [m]\n",
    "DT = 0.2  # time tick [s]\n",
    "MAXT = 5.0  # max prediction time [m]\n",
    "MINT = 4.0  # min prediction time [m]\n",
    "TARGET_SPEED = 30.0 / 3.6  # target speed [m/s]\n",
    "D_T_S = 5.0 / 3.6  # target speed sampling length [m/s]\n",
    "N_S_SAMPLE = 1  # sampling number of target speed\n",
    "ROBOT_RADIUS = 2.0  # robot radius [m]\n",
    "\n",
    "# cost weights\n",
    "KJ = 0.1\n",
    "KT = 0.1\n",
    "KD = 1.0\n",
    "KLAT = 1.0\n",
    "KLON = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):\n",
    "\n",
    "    frenet_paths = []\n",
    "\n",
    "    # generate path to each offset goal\n",
    "    for di in np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W):\n",
    "\n",
    "        # Lateral motion planning\n",
    "        for Ti in np.arange(MINT, MAXT, DT):\n",
    "            fp = Frenet_path()\n",
    "\n",
    "            lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)\n",
    "\n",
    "            fp.t = [t for t in np.arange(0.0, Ti, DT)]\n",
    "            fp.d = [lat_qp.calc_point(t) for t in fp.t]\n",
    "            fp.d_d = [lat_qp.calc_first_derivative(t) for t in fp.t]\n",
    "            fp.d_dd = [lat_qp.calc_second_derivative(t) for t in fp.t]\n",
    "            fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t]\n",
    "\n",
    "            # Loongitudinal motion planning (Velocity keeping)\n",
    "            for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S):\n",
    "                tfp = copy.deepcopy(fp)\n",
    "                lon_qp = quartic_polynomial(s0, c_speed, 0.0, tv, 0.0, Ti)\n",
    "\n",
    "                tfp.s = [lon_qp.calc_point(t) for t in fp.t]\n",
    "                tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t]\n",
    "                tfp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t]\n",
    "                tfp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t]\n",
    "\n",
    "                Jp = sum(np.power(tfp.d_ddd, 2))  # square of jerk\n",
    "                Js = sum(np.power(tfp.s_ddd, 2))  # square of jerk\n",
    "\n",
    "                # square of diff from target speed\n",
    "                ds = (TARGET_SPEED - tfp.s_d[-1])**2\n",
    "\n",
    "                tfp.cd = KJ * Jp + KT * Ti + KD * tfp.d[-1]**2\n",
    "                tfp.cv = KJ * Js + KT * Ti + KD * ds\n",
    "                tfp.cf = KLAT * tfp.cd + KLON * tfp.cv\n",
    "\n",
    "                frenet_paths.append(tfp)\n",
    "\n",
    "    return frenet_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "faTrajX = []\n",
    "faTrajY = []\n",
    "\n",
    "def calc_global_paths(fplist, csp):\n",
    "\n",
    "    #faTrajX = []\n",
    "    #faTrajY = []\n",
    "\n",
    "    for fp in fplist:\n",
    "\n",
    "        # calc global positions\n",
    "        for i in range(len(fp.s)):\n",
    "            ix, iy = csp.calc_position(fp.s[i])\n",
    "            if ix is None:\n",
    "                break\n",
    "            iyaw = csp.calc_yaw(fp.s[i])\n",
    "            di = fp.d[i]\n",
    "            fx = ix + di * math.cos(iyaw + math.pi / 2.0)\n",
    "            fy = iy + di * math.sin(iyaw + math.pi / 2.0)\n",
    "            fp.x.append(fx)\n",
    "            fp.y.append(fy)\n",
    "\n",
    "        # Just for plotting\n",
    "        faTrajX.append(fp.x)\n",
    "        faTrajY.append(fp.y)\n",
    "        \n",
    "        # calc yaw and ds\n",
    "        for i in range(len(fp.x) - 1):\n",
    "            dx = fp.x[i + 1] - fp.x[i]\n",
    "            dy = fp.y[i + 1] - fp.y[i]\n",
    "            fp.yaw.append(math.atan2(dy, dx))\n",
    "            fp.ds.append(math.sqrt(dx**2 + dy**2))\n",
    "\n",
    "        fp.yaw.append(fp.yaw[-1])\n",
    "        fp.ds.append(fp.ds[-1])\n",
    "\n",
    "        # calc curvature\n",
    "        for i in range(len(fp.yaw) - 1):\n",
    "            fp.c.append((fp.yaw[i + 1] - fp.yaw[i]) / fp.ds[i])\n",
    "\n",
    "    return fplist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "faTrajCollisionX = []\n",
    "faTrajCollisionY = []\n",
    "faObCollisionX = []\n",
    "faObCollisionY = []\n",
    "\n",
    "def check_collision(fp, ob):\n",
    "    #pdb.set_trace()\n",
    "    for i in range(len(ob[:, 0])):\n",
    "        # Calculate the distance for each trajectory point to the current object\n",
    "        d = [((ix - ob[i, 0])**2 + (iy - ob[i, 1])**2)\n",
    "             for (ix, iy) in zip(fp.x, fp.y)]\n",
    "\n",
    "        # Check if any trajectory point is too close to the object using the robot radius\n",
    "        collision = any([di <= ROBOT_RADIUS**2 for di in d])\n",
    "\n",
    "        if collision:\n",
    "            #plot(ft.x, ft.y, 'rx')\n",
    "            faTrajCollisionX.append(fp.x)\n",
    "            faTrajCollisionY.append(fp.y)\n",
    "            \n",
    "            #plot(ox, oy, 'yo');\n",
    "            #pdb.set_trace()\n",
    "            if ob[i, 0] not in faObCollisionX or ob[i, 1] not in faObCollisionY:\n",
    "                faObCollisionX.append(ob[i, 0])\n",
    "                faObCollisionY.append(ob[i, 1])\n",
    "            \n",
    "            \n",
    "            return True\n",
    "\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#faTrajOkX = []\n",
    "#faTrajOkY = []\n",
    "\n",
    "def check_paths(fplist, ob):\n",
    "\n",
    "    okind = []\n",
    "    for i in range(len(fplist)):\n",
    "        if any([v > MAX_SPEED for v in fplist[i].s_d]):  # Max speed check\n",
    "            continue\n",
    "        elif any([abs(a) > MAX_ACCEL for a in fplist[i].s_dd]):  # Max accel check\n",
    "            continue\n",
    "        elif any([abs(c) > MAX_CURVATURE for c in fplist[i].c]):  # Max curvature check\n",
    "            continue\n",
    "        elif check_collision(fplist[i], ob):\n",
    "            continue\n",
    "\n",
    "        okind.append(i)\n",
    "\n",
    "    return [fplist[i] for i in okind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fpplist = []\n",
    "\n",
    "def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob):\n",
    "    #pdb.set_trace()\n",
    "    fplist = calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0)\n",
    "    fplist = calc_global_paths(fplist, csp)\n",
    "    fplist = check_paths(fplist, ob)\n",
    "    \n",
    "    #fpplist = deepcopy(fplist)\n",
    "    fpplist.extend(fplist)\n",
    "\n",
    "    # find minimum cost path\n",
    "    mincost = float(\"inf\")\n",
    "    bestpath = None\n",
    "    for fp in fplist:\n",
    "        if mincost >= fp.cf:\n",
    "            mincost = fp.cf\n",
    "            bestpath = fp\n",
    "\n",
    "    return bestpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cubic_spline_planner import *\n",
    "def generate_target_course(x, y):\n",
    "    csp = Spline2D(x, y)\n",
    "    s = np.arange(0, csp.s[-1], 0.1)\n",
    "\n",
    "    rx, ry, ryaw, rk = [], [], [], []\n",
    "    for i_s in s:\n",
    "        ix, iy = csp.calc_position(i_s)\n",
    "        rx.append(ix)\n",
    "        ry.append(iy)\n",
    "        ryaw.append(csp.calc_yaw(i_s))\n",
    "        rk.append(csp.calc_curvature(i_s))\n",
    "\n",
    "    return rx, ry, ryaw, rk, csp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "show_animation = True\n",
    "#show_animation = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3vrGfV9q7/yIiIiIioRYZ6g743AecDpQCB4BRfjznK+A/DSxPD2K/REREREQ6hHAJ9j/F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\noUOHMmTIELp27dq+/vhH0mf/COh7Jf5EqyaTJk1i2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11775f6a0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 379
      }
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-7722e5946674>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfaTrajX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfaTrajY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m             \u001b[0;31m#pdb.set_trace()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0mfaTrajX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mfaTrajY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3316\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3317\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3318\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3319\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3320\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1890\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1891\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1892\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1893\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1894\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1781\u001b[0m         \u001b[0mReturns\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1782\u001b[0m         \"\"\"\n\u001b[0;32m-> 1783\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1784\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1785\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_set_artist_props\u001b[0;34m(self, a)\u001b[0m\n\u001b[1;32m    920\u001b[0m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 922\u001b[0;31m         \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    923\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmouseover\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmouseover_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36maxes\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m    646\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m                 self._xcid = ax.xaxis.callbacks.connect('units',\n\u001b[0;32m--> 648\u001b[0;31m                                                         self.recache_always)\n\u001b[0m\u001b[1;32m    649\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m                 self._ycid = ax.yaxis.callbacks.connect('units',\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/site-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self, s, func)\u001b[0m\n\u001b[1;32m    496\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mcalled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m         \"\"\"\n\u001b[0;32m--> 498\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_func_cid_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWeakKeyDictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    499\u001b[0m         \u001b[0;31m# Note proxy not needed in python 3.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;31m# TODO rewrite this when support for python2.x gets dropped.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/fjp/anaconda/lib/python3.5/weakref.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dict)\u001b[0m\n\u001b[1;32m    323\u001b[0m     \"\"\"\n\u001b[1;32m    324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    326\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselfref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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FISwsDK6urkKXQ0gvCvwCocBPCNGn/oIyAIMNz8Ot3v588803BjGWn+M4nT1BNzMzg7+/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\nNzMzMzMr2P8B8eaGJd7P2ysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11775f6a0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# way points\n",
    "wx = [0.0, 10.0, 20.5, 35.0, 70.5]\n",
    "wy = [0.0, -6.0, 5.0, 6.5, 0.0]\n",
    "# obstacle lists\n",
    "ob = np.array([[20.0, 10.0],\n",
    "               [30.0, 6.0],\n",
    "               [30.0, 8.0],\n",
    "               [35.0, 8.0],\n",
    "               [50.0, 3.0]\n",
    "               ])\n",
    "\n",
    "tx, ty, tyaw, tc, csp = generate_target_course(wx, wy)\n",
    "\n",
    "# initial state\n",
    "c_speed = 10.0 / 3.6  # current speed [m/s]\n",
    "c_d = 2.0  # current lateral position [m]\n",
    "c_d_d = 0.0  # current lateral speed [m/s]\n",
    "c_d_dd = 0.0  # current latral acceleration [m/s]\n",
    "s0 = 0.0  # current course position\n",
    "\n",
    "area = 20.0  # animation area length [m]\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "faTx = tx\n",
    "faTy = ty\n",
    "faObx = ob[:, 0]\n",
    "faOby = ob[:, 1]\n",
    "faPathx = []\n",
    "faPathy = []\n",
    "faRobotx = []\n",
    "faRoboty = []\n",
    "faSpeed = []\n",
    "\n",
    "for i in range(100):\n",
    "    path = frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob)\n",
    "\n",
    "    s0 = path.s[1]\n",
    "    c_d = path.d[1]\n",
    "    c_d_d = path.d_d[1]\n",
    "    c_d_dd = path.d_dd[1]\n",
    "    c_speed = path.s_d[1]\n",
    "\n",
    "    if np.hypot(path.x[1] - tx[-1], path.y[1] - ty[-1]) <= 1.0:\n",
    "        print(\"Goal\")\n",
    "        break\n",
    "\n",
    "\n",
    "    faPathx.append(path.x[1:])\n",
    "    faPathy.append(path.y[1:])\n",
    "    faRobotx.append(path.x[1])\n",
    "    faRoboty.append(path.y[1])\n",
    "    faSpeed.append(c_speed)\n",
    "    \n",
    "    \n",
    "    if show_animation:\n",
    "        plt.cla()\n",
    "        plt.plot(tx, ty, animated=True)\n",
    "        plt.plot(ob[:, 0], ob[:, 1], \"xk\")\n",
    "        \n",
    "        plt.plot(path.x[1], path.y[1], \"vc\")\n",
    "        \n",
    "        for (ix, iy) in zip(faTrajX, faTrajY):\n",
    "            #pdb.set_trace()\n",
    "            plt.plot(ix[1:], iy[1:], '-', color=[0.5, 0.5, 0.5])\n",
    "        faTrajX = []\n",
    "        faTrajY = []\n",
    "        \n",
    "        for (ix, iy) in zip(faTrajCollisionX, faTrajCollisionY):\n",
    "            #pdb.set_trace()\n",
    "            plt.plot(ix[1:], iy[1:], 'rx')\n",
    "        faTrajCollisionX = []\n",
    "        faTrajCollisionY = []\n",
    "        #pdb.set_trace()\n",
    "        for fp in fpplist:\n",
    "            #pdb.set_trace()\n",
    "            plt.plot(fp.x[1:], fp.y[1:], '-g')\n",
    "        fpplist = []\n",
    "        \n",
    "        #pdb.set_trace()\n",
    "        for (ix, iy) in zip(faObCollisionX, faObCollisionY):\n",
    "            #pdb.set_trace()\n",
    "            plt.plot(ix, iy, 'oy')\n",
    "        faObCollisionX = []\n",
    "        faObCollisionY = []\n",
    "        \n",
    "        plt.plot(path.x[1:], path.y[1:], \"-ob\")\n",
    "        \n",
    "        plt.xlim(path.x[1] - area, path.x[-1] + area)\n",
    "        plt.ylim(path.y[1] - area, path.y[-1] + area)\n",
    "        plt.title(\"v[km/h]:\" + str(c_speed * 3.6)[0:4])\n",
    "        plt.grid(True)\n",
    "        #plt.pause(0.0001)\n",
    "        display.clear_output(wait=True)\n",
    "        display.display(pl.gcf())\n",
    "        \n",
    "\n",
    "#print(\"Finish\")\n",
    "#if show_animation:\n",
    "    #plt.grid(True)\n",
    "    #plt.pause(0.0001)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DEPYAADAAYQ8AAAMQ9gAAMABhDwAAAxD2AAAwAGEPAAADEPYAADAAYQ8AAAMQ9gAAMABh\nDwAAAxD2AAAwAGEPAAADEPYAADAAYQ8AAAMQ9gAAMABhDwAAAxD2AAAwAGEPAAADEPYAADAAYQ8A\nAAMQ9gAAMABhDwAAAxD2AAAwAGEPAAADEPYAADAAYQ8AAAMQ9gAAMABhDwAAAxD2AAAwAGEPAAAD\nEPYAADCAlQv7qjqsqnoHl/MWPSMAAMzbAYseYC98Msm711n/qXkPAgAAi7bKYX9Zd5+56CEAAGAZ\nrNypOAAAwA9b5SP2962qZyW5d5Lrknysuy9f8EwAALAQqxz2vzhdvq+qPpzktO7+4q4+SFVt2WDT\nkXs+GgAAzNcqnopzS5I/S3JskkOmy4lJLkxyUpIPVtVBC5sOAAAWYCFH7Ktqa5Kf3o27/E13PzVJ\nuvvrSf5ku+0XVdVjk1yS5JFJTk/y+l154O4+doMZtyQ5ZjdmBACAhVnUqTifS3Lbbtz+Kzu7QXff\nXlVnZRb2J2QXwx4AAEawkLDv7lP20UN/Y1o6FQcAgP3KKp5jvyOPmpafX+gUAAAwZysX9lV1TFX9\n0NxVdUqSF0xXz53vVAAAsFir+HaXr0ny4Kq6NMmXpnUPTXLy9PXLu/vShUwGAAALsophf06SJyX5\n+SSPT3KXJF9L8rdJ3tTdFy9wNgAAWIiVC/vufluSty16DgAAWCYrd449AADww4Q9AAAMQNgDAMAA\nhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9\nAAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAA\nDEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA\n2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxg4WFfVXepqudX1Tuq6rKq+k5VdVWdvgv3\nPa2q/rmqbq6qG6vqw1X1y/OYGwAAlsnCwz7JQUlel+TpSQ5Ncs2u3KmqXp3k7CT3SfLXSc5N8rNJ\n3ltVf7AvBgUAgGW1DGF/S5InJLlvdx+a5O07u0NVHZfkRUk+l+Sh3f2C7n5OkmOTXJ/k1VV12D6b\nGAAAlszCw767v9Pd7+vur+7G3Z49Lf+8u/9tzWNtTfLmJHdN8ozNmxIAAJbbAYseYA+dPC3fv862\n9yV5+XSbV+zsgapqywabjtyz0QAAYP4WfsR+d1XVQUnul+TmDY7yf3ZaHj6/qQAAYLFW8Yj9wdPy\nxg22b1t/z115sO4+dr3105H8Y3ZvNAAAWIxNOWJfVVunt6jc1cu5m/F9AQCAmc06Yv+5JLftxu2/\nshffa9sR+YM32L5t/Q178T0AAGClbErYd/cpm/E4u/i9vlVVX05yv6q6zzrn2T94Wl45r5kAAGDR\nVu7Fs5MPTcvHrbPt8dvdBgAAhreqYf/WafnSqjpk28rpQ6mek+TbSd4x/7EAAGAxluJdcarqJbnj\nfeOPnpbPqKpfmL6+pLvP2nb77r60ql6T5IVJLq+q85P8SJLfSnKvJM+dPqwKAAD2C0sR9pmdUnPi\nduuOmy7bnLV2Y3e/qKr+X2ZH6P9Lku8l+USSv+zuf9iHswIAwNJZirDv7pP28H5nJzl7M2cBAIBV\ntKrn2AMAAGsIewAAGICwBwCAAQh7AAAYgLAHAIABCHsAABiAsAcAgAEIewAAGICwBwCAAQh7AAAY\ngLAHAIABCHsAABiAsAcAgAEIewAAGICwBwCAAQh7AAAYgLAHAIABCHsAABiAsAcAgAEIewAAGICw\nBwCAAQh7AAAYgLAHAIABCHsAABiAsAcAgAEIewAAGICwBwCAAQh7AAAYgLAHAIABCHsAABiAsAcA\ngAEIewAAGICwBwCAAQh7AAAYgLAHAIABCHsAABiAsAcAgAEIewAAGICwBwCAASw87KvqLlX1/Kp6\nR1VdVlXfqaquqtN3cJ+nT7fZ6PLsef4ZAABg0Q5Y9ABJDkryuunrryW5Jsn9d/G+70ly2TrrP74J\ncwEAwMpYhrC/JckTklzW3V+tqjOTvGIX7/vu7j57Xw0GAACrYuFh393fSfK+Rc8BAACrbOFhv5eO\nrqozkhyY5MtJLuzuLy14JgAAmLtVD/vnb3f9u1V1VpIzuvu2XXmAqtqywaYj92oyAACYo4W/K84e\nujrJc5MckdmLb++b5DeTbE3yrCRvX9hkAACwAJtyxL6qtib56d24y99091P39Pt190eSfGTNqluS\n/F1V/VOSTyb57ar6b939yV14rGPXWz8dyT9mT2cEAIB52qxTcT6XZJdOfZl8ZZO+7w/o7n+tqguS\n/E6SEzKLfAAAGN6mhH13n7IZj7NJvjEtD1roFAAAMEereo79jjxyWn5+oVMAAMAcrWTYV9XD11l3\np6r64ySPTnJtkvfPfTAAAFiQpXi7y6p6Se54e8mjp+UzquoXpq8v6e6z1tzl/1bVpzI7h/7LSQ5O\ncnySh2T2Qtrf6e6b9v3kAACwHJYi7JM8LsmJ2607brpsszbsX53kEUlOTnKvJN9L8sUkb07ymu52\nGg4AAPuVpQj77j5pN2//h/toFAAAWEkreY49AADwg4Q9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAA\nDEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA\n2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgD\nAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDA\nAIQ9AAAMQNgDAMAAhD0AAAxA2AMAwACEPQAADEDYAwDAAIQ9AAAMoLp70TMspaq67m53u9u9jjrq\nqEWPAgDAoK644orceuut13f3vff2sYT9Bqrq6iT3SLJ1waMkyZHT8tMLnWIc9ufmsj83j325uezP\nzWV/bi77c/Os+r48LMlN3f2AvX0gYb8CqmpLknT3sYueZQT25+ayPzePfbm57M/NZX9uLvtz89iX\nd3COPQAADEDYAwDAAIQ9AAAMQNgDAMAAhD0AAAzAu+IAAMAAHLEHAIABCHsAABiAsAcAgAEIewAA\nGICwBwCAAQh7AAAYgLAHAIABCPslVVV3qarnV9U7quqyqvpOVXVVnb6D+zx9us1Gl2fP88+wTPZk\nf66572lV9c9VdXNV3VhVH66qX57H3Kumqg7byXPwvEXPuIyq6ier6u1V9ZWq+nZVba2q11XVIYue\nbdVM+26j5981i55vGVXVqVX1xqq6uKpumvbVuTu5z3FVdUFVXV9Vt1bV5VV1RlXdeV5zL6vd2Z9+\nZu5YVd27qk6vqr+vqqum59qNVXVJVT2zqtbt2P35+XnAogdgQwcled309deSXJPk/rt43/ckuWyd\n9R/fhLlW1R7tz6p6dZIXJflSkr9O8iNJnpLkvVX13O5+074Zd+V9Msm711n/qXkPsuyq6kFJLk3y\n45n93f10kkckeX6Sx1XV8d193QJHXEU35o6/72vdPO9BVsTLkjwss/3zpSRH7ujGVfVrSd6V5LYk\n70xyfZJfSfLaJMcnefK+HHYF7Nb+nPiZub4nJ3lLkq8muTDJF5P8RJJfT3JWksdX1ZN7zaet7vfP\nz+52WcJLZgH5+CT3ma6fmaSTnL6D+zx9us3TFz3/sl32cH8eN93mqiSHrFl/WJLrMvuhcdii/2zL\ndJn2TSc5e9GzrMolyT9O++y5261/zbT+rYuecZUuSbYm2broOVbpkuQxSR6cpJKcND3vzt3gtvdI\n8vUk307y8DXrD8zsP6id5CmL/jOt0P70M3PH+/LkzKL8TtutPzSzyO8kv7Fm/X7//HQqzpLq7u90\n9/u6+6uLnmUEe7g/t5269Ofd/W9rHmtrkjcnuWuSZ2zelOxvpqP1j80sRt+83eZXJPlWkqdV1UFz\nHo39SHdf2N2f7amAduLUJD+W5Lzu/v5vgbv7tsyOVCfJ7++DMVfGbu5PdqC7P9Td7+3u7223/pok\nb52unrRm037//HQqzpiOrqozMvsf6peTXNjdX1rwTKvo5Gn5/nW2vS/Jy6fbvGJuE62O+1bVs5Lc\nO7Pfbnysuy9f8EzL6DHT8gPr/MP1zar6aGbh/6gkH5z3cCvsrlX11CQ/ldl/ji5PclF3f3exYw1h\nRz8XL0pyS5Ljququ3f3t+Y218vzM3H3/Pi1vX7Nuv39+CvsxPX+769+tqrOSnDH9r5WdmI6Q3i/J\nzRsc5f/stDx8flOtlF+cLt9XVR9Oclp3f3EhEy2nI6bllRts/2xmYX94hP3uODTJOdutu7qqntHd\nH1nEQAPZ8Dnb3bdX1dVJfibJA5NcMc/BVpyfmbuhqg5I8rvT1bURv98/P52KM5arkzw3syf2QUnu\nm+Q3M/s1/7OSvH1hk62eg6fljRts37b+nnOYZZXckuTPkhyb5JDpcmJmL3o6KckHnVbyAzzPNt87\nkpySWdwflORnk/xVZucyv6+qHra40YbgObu5/MzcM3+R5CFJLujuf1yzfr9/fgr7fWgnb7u23mWH\nby+2M939ke5+U3df2d23dPdXu/vvMvt1/78l+e1V/kdt3vtzf7U3+7m7v97df9Ldn+juG6bLRZkd\ndf4/Sf5jkp2+xSjsqe5+5XRe7temn4Of6u5nZ/Zi5Ltl9sJ5WAp+Zu6+qnpeZu9W9+kkT1vwOEvH\nqTj71ucye+eUXfW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188258d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 379
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import animation\n",
    "\n",
    "fig, ax = plt.subplots();\n",
    "\n",
    "ax.set_xlim((faPathx[1][1] - area, faPathx[1][1] + area))\n",
    "ax.set_ylim((faPathy[1][1] - area, faPathy[1][1] + area))\n",
    "\n",
    "h_ref = ax.plot([], [], \"-b\") # reference path\n",
    "h_obstacles = ax.plot([], [], \"xk\") #obstacles to animate\n",
    "h_path = ax.plot([], [], \"-or\") # trajectory to animate\n",
    "h_car = ax.plot([], [], \"vc\")\n",
    "\n",
    "\n",
    "patches = list(h_obstacles) + list(h_path) + list(h_ref) + list(h_car) #things to animate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def init():\n",
    "    #init lines\n",
    "    h_ref[0].set_data(faTx, faTy)\n",
    "    \n",
    "    h_obstacles[0].set_data(faObx, faOby)\n",
    "    \n",
    "    h_path[0].set_data([], [])\n",
    "    \n",
    "    h_car[0].set_data([], [])\n",
    "    \n",
    "    \n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    \n",
    "    ax.grid(True)\n",
    "    \n",
    "    \n",
    "    return patches #return everything that must be updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# animation function. This is called sequentially\n",
    "def animate(i):\n",
    "\n",
    "\n",
    "    ax.set_xlim((faPathx[i][1] - area, faPathx[i][1] + area))\n",
    "    ax.set_ylim((faPathy[i][1] - area, faPathy[i][1] + area))\n",
    "\n",
    "\n",
    "    h_path[0].set_data(faPathx[i], faPathy[i])\n",
    "\n",
    "    h_car[0].set_data(faRobotx[i], faRoboty[i])\n",
    "    \n",
    "    \n",
    "    ax.set_title(\"v[km/h]:\" + str(faSpeed[i] * 3.6)[0:4], fontsize=12)\n",
    "\n",
    "    return patches #return everything that must be updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nFrames = len(faPathx)\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=nFrames, interval=50, blit=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "MTAw\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
